The Role
Design, build, and operate production ML systems and LLM-based agents for fraud and risk detection at a global crypto exchange. Responsibilities span feature engineering, model training and serving, monitoring, explainability, integration with operations, and building safe, auditable LLM agents for investigation and automation.
Summary Generated by Built In
Who We Are
At OKX, we believe that the future will be reshaped by crypto, and ultimately contribute to every individual's freedom.
OKX is a leading crypto exchange, and the developer of OKX Wallet, giving millions access to crypto trading and decentralized crypto applications (dApps). OKX is also a trusted brand by hundreds of large institutions seeking access to crypto markets. We are safe and reliable, backed by our Proof of Reserves.
Across our multiple offices globally, we are united by our core principles: We Before Me, Do the Right Thing, and Get Things Done. These shared values drive our culture, shape our processes, and foster a friendly, rewarding, and diverse environment for every OK-er.
OKX is part of OKG, a group that brings the value of Blockchain to users around the world, through our leading products OKX, OKX Wallet, OKLink and more.
About The OpportunityBuilding machine learning systems for risk at a global crypto exchange is fundamentally different from conventional ML engineering. The data spans on-chain activity, fiat deposits and withdrawals, trading behaviour, account access patterns, device intelligence, identity information, and customer interactions—signals that very few organizations can analyze together.
The problems are complex, adversarial, and constantly evolving. Models must identify emerging fraud patterns, scams, account takeovers, payment abuse, and other forms of financial risk while minimizing disruption to legitimate customers. Success is not measured only through offline model metrics. It is measured through prevented losses, improved approval rates, reduced false positives, faster investigations, and more reliable customer experiences.
This role sits within a multidisciplinary risk team of machine learning engineers, data scientists, risk strategy specialists, analytics engineers, product managers, and operations teams. You will work across the full ML lifecycle—from problem formulation, feature engineering, and model development to real-time deployment, monitoring, experimentation, and continuous iteration.
You will also help shape how AI is used across the risk organization. LLM-assisted development, automated model workflows, AI-powered investigations, and intelligent review agents are part of the team’s daily work. We are looking for engineers who already use these tools effectively and can help establish safe, scalable, and production-ready AI practices.
What You’ll Be Doing
- Design, build, and deploy machine learning models for risk use cases such as payment fraud, account takeover, scam detection, deposit and withdrawal risk, promotional abuse, customer risk assessment, and transaction monitoring.
- Own production ML systems end to end, including feature pipelines, training workflows, model serving, decision integrations, monitoring, alerting, drift detection, retraining, and incident response.
- Partner with risk strategy and product teams to translate models into effective production controls, including approval, rejection, review, cooldown, limit adjustment, account restriction, and other risk mitigation actions.
- Work closely with risk operations teams to understand investigation workflows, incorporate reviewer feedback, improve model explainability, and continuously refine labels and training data.
- Apply AI-assisted development throughout the engineering workflow, using LLM coding tools to accelerate implementation, testing, debugging, analysis, and documentation while maintaining appropriate security and review standards.
- Develop AI-powered risk capabilities such as investigation agents, case summarization, evidence collection, review recommendations, alert triage, suspicious-entity mining, and automated decision support.
- Take research-stage models into reliable production systems by validating feature logic, reviewing data quality, addressing latency and scalability constraints, and ensuring consistency between offline training and online inference.
- Ensure models and decision systems are explainable, traceable, and well documented so that model outputs can be understood by risk operations, product stakeholders, internal governance teams, and regulators where applicable.
- Design, build, and deploy LLM-based agents for risk operations and investigation workflows, including case triage, evidence retrieval, transaction analysis, alert summarization, review recommendations, and automated action orchestration.
- Develop production-grade agent architectures using tool calling, retrieval-augmented generation, workflow orchestration, structured outputs, memory, guardrails, and human-in-the-loop controls.
- Build evaluation frameworks for LLM agents, measuring factual accuracy, task completion, decision consistency, latency, cost, reviewer acceptance, and operational impact. Ensure LLM agents operate safely in a regulated risk environment by implementing permission controls, audit logs, data privacy protections, prompt and tool security, fallback mechanisms, and clear escalation paths.
What We Look For in You
- Significant professional experience in machine learning engineering, applied data science, or a closely related field, with a strong record of taking models from prototype to production. Scope and level will be calibrated based on experience.
- Strong Python skills and hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, XGBoost, LightGBM, or scikit-learn.
- Strong knowledge of applied machine learning fundamentals, including supervised learning, anomaly detection, representation learning, class-imbalanced modeling, model calibration, and evaluation under changing data distributions.
- Demonstrable fluency with AI-assisted engineering. You regularly use LLM coding tools, have built AI-integrated workflows or applications, and understand both the productivity benefits and the security, reliability, and governance risks.
- Familiarity with model explainability techniques such as SHAP, feature attribution, reason-code generation, and model scorecards.
- Hands-on experience designing and deploying production LLM agents, including agentic workflows, tool calling, retrieval-augmented generation, prompt and context management, structured output generation, and multi-step task orchestration.
- Experience integrating LLM agents with internal systems, APIs, databases, search tools, case-management platforms, or decision engines to automate complex operational workflows.
- A strong understanding of LLM-agent evaluation and reliability, including hallucination control, grounding, observability, permissions, failure handling, human review, latency, and cost optimization.
- Experience building AI agents for fraud, risk, compliance, customer operations, cybersecurity, or other high-stakes domains is a meaningful advantage.
- Strong communication and collaboration skills, with the ability to work effectively with engineers, data scientists, risk specialists, product managers, operations teams, and legal or compliance stakeholders.
The base salary range for this position is $214,666 to $321,999. The salary offered depends on a variety of factors, including job-related knowledge, skills, experience, and market location. In addition to the salary, a performance bonus and long-term incentives may be provided as part of the compensation package, as well as a full range of medical, financial, and/or other benefits, dependent on the position offered. Applicants should apply via Okcoin and OKX internal or external careers site.
OKX is committed to equal employment opportunities regardless of race, color, genetic information, creed, religion, sex, sexual orientation, gender identity, lawful alien status, national origin, age, marital status, and non-job related physical or mental disability, or protected veteran status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Notice:
All official OKX vacancies are published on this website. While roles may appear on selected third-party platforms from time to time, information on other sites may be inaccurate or outdated. If in doubt, please apply directly through our official careers website.
Information collected and processed as part of the recruitment process of any job application you choose to submit is subject to OKX's Candidate Privacy Notice.
Skills Required
- Significant professional experience in machine learning engineering or applied data science with a strong record of taking models from prototype to production.
- Strong Python skills.
- Hands-on experience with PyTorch, TensorFlow, XGBoost, LightGBM, or scikit-learn.
- Experience owning production ML systems end-to-end: feature pipelines, training workflows, model serving, monitoring, drift detection, retraining, and incident response.
- Strong knowledge of applied ML fundamentals: supervised learning, anomaly detection, representation learning, class-imbalanced modeling, calibration, and evaluation under distribution shift.
- Demonstrable fluency with AI-assisted engineering and LLM coding tools.
- Familiarity with model explainability techniques such as SHAP, feature attribution, reason-code generation, and model scorecards.
- Hands-on experience designing and deploying production LLM agents, including agentic workflows, tool calling, RAG, prompt/context management, structured outputs, and human-in-the-loop controls.
- Experience integrating LLM agents with internal systems, APIs, databases, search tools, case-management platforms, or decision engines.
- Strong understanding of LLM-agent evaluation and reliability: hallucination control, grounding, observability, permissions, failure handling, and cost/latency optimization.
- Experience building AI agents for fraud, risk, compliance, customer operations, cybersecurity, or other high-stakes domains.
- Strong communication and collaboration skills working with cross-functional teams including product, operations, legal, and compliance.
OKX Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about OKX and has not been reviewed or approved by OKX.
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Fair & Transparent Compensation — Pay is considered competitive or above market, especially in engineering, product, and legal roles across major hubs. This positioning is consistently cited as a major attraction for candidates.
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Healthcare Strength — Role descriptions indicate comprehensive medical, dental, vision, life, and disability coverage, with employer-paid premiums in some cases. Health coverage is highlighted alongside core benefits like PTO and parental leave.
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Wellbeing & Lifestyle Benefits — Allowances for education and fitness, meal perks and snacks, team-building budgets, and structured learning programs are described across locations. These extras enhance the total rewards package beyond base pay.
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The Company
What We Do
Founded in 2017, OKX is one of the world’s leading cryptocurrency spot and derivatives exchanges. OKX innovatively adopted blockchain technology to reshape the financial ecosystem by offering some of the most diverse and sophisticated products, solutions, and trading tools on the market. Trusted by more than 20 million users in over 180 regions globally, OKX strives to provide an engaging platform that empowers every individual to explore the world of crypto. In addition to its world-class DeFi exchange, OKX serves its users with OKX Insights, a research arm that is at the cutting edge of the latest trends in the cryptocurrency industry. With its extensive range of crypto products and services, and unwavering commitment to innovation, OKX’s vision is a world of financial access backed by blockchain and the power of decentralized finance.
